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An Expectation–Maximization-Based IVA Algorithm for Speech Source Separation Using Student’s t Mixture Model Based Source Priors

1
Department of Informatics, King’s College London, London WC2B 4BG, UK
2
Department of Engineering, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Acoustics 2019, 1(1), 117-136; https://doi.org/10.3390/acoustics1010009
Received: 23 November 2018 / Revised: 21 December 2018 / Accepted: 29 December 2018 / Published: 10 January 2019
The performance of the independent vector analysis (IVA) algorithm depends on the choice of the source prior to better model the speech signals as it employs a multivariate source prior to retain the dependency between frequency bins of each source. Identical source priors are frequently used for the IVA methods; however, different speech sources will generally have different statistical properties. In this work, instead of identical source priors, a novel Student’s t mixture model based source prior is introduced for the IVA algorithm that can adapt to the statistical properties of different speech sources and thereby enhance the separation performance of the IVA algorithm. The unknown parameters of the source prior and unmixing matrices are estimated together by deriving an efficient expectation maximization (EM) algorithm. Useful improvement in the separation performance in different realistic scenarios is confirmed by experimental studies on real datasets. View Full-Text
Keywords: blind source separation; student’s t mixture model; independent vector analysis; real room impulse responses blind source separation; student’s t mixture model; independent vector analysis; real room impulse responses
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Rafique, W.; Chambers, J.; Sunny, A.I. An Expectation–Maximization-Based IVA Algorithm for Speech Source Separation Using Student’s t Mixture Model Based Source Priors. Acoustics 2019, 1, 117-136.

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